Senior Data Scientist

CTI Staffing

. Work with large and complex data sets to solve unstructured problems using analytical and statistical approaches for multiple products independently .

Posted 10/7/2026full-timeRemote • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in statistical modeling, data analysis, and machine learning techniques, with a strong focus on data stability and model performance. Proficient in SQL and various statistical software, capable of leading teams and communicating complex data insights effectively.

Highest-signal resume keywords

Statistical ModelingMachine LearningData AnalysisSQL ProficiencyData Cleaning

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Statistical TechniquesData Science PrinciplesBayesian ModelingClassification ModelsCluster AnalysisNeural NetworksNon-parametric MethodsMultivariate StatisticsAB TestingHypothesis Testing

Soft Skills

Team LeadershipCommunicationStakeholder EngagementProblem SolvingMentoring

Tools & Technologies

Statistical SoftwareHybrid DatabasesCloud DatabasesSQLNSQL

Certifications & Qualifications

Master’s Degree in StatisticsDoctorate in EconomicsDoctorate in Finance

Industry Keywords

Data DriftModel RefinementEconometric TechniquesTime-Series AnalysisPanel Data MethodsLogistic RegressionRisk ManagementRegulatory StandardsInternal Control StandardsAudit Compliance

Tech Stack

Tools & technologies

CloudSQL

About the role

Key responsibilities & impact

Work with large and complex data sets to solve unstructured problems using analytical and statistical approaches for multiple products independently
Lead sourcing, ingesting, and cleaning data sets in preparation for analysis
Assist more experienced data scientists to productionize and scale data cleanup processes
Ensure data stability, accounting for complex data drift in development and production
Build econometric, statistical, and machine learning models for classification, clustering, pattern analysis, sampling, and simulations
Commit complex code into the model repository and promote complex models into production
Develop champion/challenger models and adjust models accordingly
Implement a framework for building self-healing models
Select and refine models based on performance, reliability, stability metrics, and business feedback
Draft model refinement educational materials for data users
Create model outputs for business discussions to display outcomes, impact, and business value
Lead less experienced team members in creating consumable model outputs
Attend stakeholder meetings to discuss concerns, opportunities, and production challenges
Work with more experienced data scientists to develop new research approaches and assess adaptation based on client needs
Review code for efficiency, accuracy, and best practices
Adhere to company risk and regulatory standards, policies, controls, and risk appetite
Identify risk-related issues requiring escalation to management
Promote an environment supporting diversity and reflecting the Bank brand
Maintain internal control standards and implement audit and regulatory action items
Complete other related duties as assigned

Requirements

What you’ll need

Bachelor’s degree and a minimum of 5 years related experience, or in lieu of a degree, a combined minimum of 9 years higher education and/or work experience, including a minimum of 5 years related experience
Experience working with multiple statistics and data science principles such as AB testing, sample selection, hypothesis testing, and modeling bias
Proficiency with pertinent statistical software, languages, and tools
Experience with various hybrid databases both on premise and in the cloud
Intermediate level knowledge of Structured Query Language (SQL) and Not Only Structured Query Language (nSQL)
Intermediate understanding of modeling techniques such as Bayesian Modeling, Classification models, Cluster analysis, Neural Network, Non-parametric methods, and Multivariate statistics
Experience analyzing large data sets
Master’s of Science or Doctorate degree in Statistics, Economics, Finance, or related field is preferred
Fluent in econometric/statistical techniques, including time-series analysis, panel data methods, and logistic regression
Tactical experience with pertinent statistical software, languages, and tools

Benefits

Comp & perks

Remote work arrangement